Affiliation:
1. The University of Paisley, Scotland, UK
Abstract
We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
Publisher
World Scientific Pub Co Pte Lt
Subject
Computer Networks and Communications,General Medicine
Cited by
52 articles.
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